Evidence vs Metabase vs Apache Superset: Best Open-Source BI Tool (2026)

Business intelligence doesn't have to cost $70K/year for Tableau or Looker. Open-source BI tools have caught up — and in some ways surpassed — their commercial counterparts. Evidence (code-first), Metabase (no-code), and Apache Superset (enterprise open-source) represent three philosophies.

Quick Comparison

Feature	Evidence	Metabase	Apache Superset
Approach	Code-first (Markdown + SQL)	No-code GUI	GUI + SQL
Target user	Data teams, developers	Business users, analysts	Data teams, analysts
Deployment	Static site (build + deploy)	Docker / Cloud	Docker
Visualizations	Markdown reports with charts	Interactive dashboards	Interactive dashboards
SQL	Write SQL in Markdown files	Visual query builder + SQL	SQL Lab + visual builder
Collaboration	Git (PRs, version control)	Built-in sharing	Built-in sharing
Self-host	Easy (static files)	Easy (Docker)	Moderate (Docker)
Cloud option	Evidence Cloud	Metabase Cloud ($85/mo)	Preset (managed Superset)
License	MIT	AGPL	Apache 2.0

Evidence: Code-First BI

Evidence treats BI reports like code. You write SQL queries in Markdown files, add chart components, and build to a static site. Think of it as "Jekyll for dashboards."

How It Works

# Monthly Revenue Report

```sql revenue_by_month
select date_trunc('month', created_at) as month,
       sum(amount) as revenue
from orders
group by 1
order by 1

Revenue has been trending {revenue_by_month[revenue_by_month.length-1].revenue > revenue_by_month[revenue_by_month.length-2].revenue ? 'up' : 'down'} this month.

<LineChart data={revenue_by_month} x=month y=revenue/> ```

Strengths

Version control. Reports are files. Use Git for version history, pull requests for review, and CI/CD for deployment. Know exactly who changed what and when.

Reproducible. Reports build deterministically from SQL queries. No hidden dashboard state or cached results confusing things.

Beautiful output. Evidence produces polished, readable reports. More like a data blog than a dashboard. Great for board reports, investor updates, and stakeholder presentations.

Static deployment. Build once → deploy anywhere (Vercel, Netlify, S3). No running server required. Fast, cheap, and reliable.

Templating. Use JavaScript in Markdown for conditional content, loops, and dynamic narratives. "Revenue increased 23% month-over-month" generated automatically.

Weaknesses

• No interactivity. Static output means no drill-down, no real-time filters, no ad-hoc exploration. Reports are read-only.
• Developer-only. Business users can't create or modify reports without writing Markdown and SQL.
• No embedded analytics. Can't embed interactive dashboards in your product.
• Limited visualizations. Chart types are good but fewer than Superset or Metabase.
• Batch, not real-time. Data refreshes when you rebuild, not in real-time.

Best For

Data teams that want version-controlled, reproducible reports. Stakeholder communication (board decks, investor updates, weekly metrics). Teams that think in code, not GUIs.

Metabase: No-Code BI for Everyone

Metabase is the most accessible open-source BI tool. Business users can explore data and create dashboards without writing SQL.

Strengths

No-code query builder. Click through tables, apply filters, group by columns, and visualize — all without SQL. Non-technical team members can self-serve.

Beautiful out of the box. Metabase's default charts and dashboards look professional without customization.

Quick setup. Docker container, point at your database, done. First dashboard in 15 minutes.

Interactive dashboards. Filters, drill-down, click-to-explore. Users can slice data without creating new queries.

Embedding. Embed dashboards and questions directly in your product. White-labeled and interactive.

Question and model system. Save queries as "questions" and build "models" that other questions can reference. Builds a semantic layer over your raw database.

Weaknesses

• AGPL license. If you embed Metabase in your product, AGPL's copyleft provisions apply. Metabase's commercial embedding requires a paid license ($85+/month).
• Limited for complex analysis. The no-code builder handles common queries but struggles with complex joins, CTEs, and window functions. SQL mode exists but isn't the primary UX.
• Performance at scale. With many users and complex dashboards, Metabase can slow down. Requires caching configuration and sometimes a dedicated database.
• Limited customization. Dashboard layout and chart options are less flexible than Superset.
• Self-hosted maintenance. Upgrades, backups, and scaling require attention.

Best For

Teams where non-technical users need data access. Startups wanting quick analytics. Products needing embedded analytics.

Apache Superset: Enterprise Open-Source

Apache Superset is the most powerful open-source BI platform. It's used by Airbnb (who created it), Twitter, and many enterprises.

Strengths

Most visualization types. 40+ chart types including geospatial maps, treemaps, heatmaps, and custom plugins. If Superset can't visualize it, you probably don't need it.

SQL Lab. Full SQL IDE built into the platform. Write, run, and visualize queries. Save as charts and add to dashboards. Power users love it.

Enterprise features. Row-level security, role-based access, audit logging, and caching. Ready for large organizations with complex permission requirements.

Database support. Connects to 30+ databases including Snowflake, BigQuery, Redshift, ClickHouse, Druid, and every major SQL database.

Apache license. Permissive license with no copyleft restrictions. Embed freely.

Active development. Large contributor community. Regular releases with new features.

Weaknesses

• Complex setup. Docker Compose with Redis, PostgreSQL (metadata), and Celery workers. More ops overhead than Metabase.
• Steep learning curve. Powerful but not intuitive. Business users often need training.
• UI is functional, not beautiful. Dashboards look adequate but less polished than Metabase's defaults.
• Documentation varies. Core docs are good but advanced topics often require community searching.
• Resource-heavy. Requires more infrastructure than Metabase for equivalent workloads.

Best For

Data teams needing powerful visualization and SQL exploration. Large organizations with complex security requirements. Teams connecting to cloud data warehouses.

Feature Deep Dive

Data Sources

Source	Evidence	Metabase	Superset
PostgreSQL	✅	✅	✅
MySQL	✅	✅	✅
BigQuery	✅	✅	✅
Snowflake	✅	✅	✅
SQLite	✅	✅	✅
DuckDB	✅	❌	Plugin
CSV/Parquet	✅ (DuckDB)	❌	✅
25+ others	Some	✅	✅

Collaboration

Evidence: Git-based. PRs, code review, branch-based development. Best for engineering-culture teams.

Metabase: Built-in sharing, collections, and permissions. Best for mixed technical/non-technical teams.

Superset: Dashboard-level permissions, role-based access. Best for large organizations with formal data governance.

Hosting Costs

Evidence: Near-zero. Static files on Vercel/Netlify (free). Build step needs a CI runner.

Metabase: Small — single Docker container. Runs on a $10-20/month VPS. Or Metabase Cloud from $85/month.

Superset: Moderate — multiple containers (Superset, Redis, PostgreSQL, Celery). $30-100/month for self-hosted. Or Preset from ~$20/user/month.

FAQ

Which is easiest to set up?

Metabase (Docker + database connection = done in 15 minutes). Evidence requires coding setup. Superset requires Docker Compose configuration.

Can non-technical users use Evidence?

No. Evidence requires SQL and Markdown. It's designed for data teams who build reports for others to read. Metabase is the choice for non-technical self-serve analytics.

Is Metabase's AGPL license a problem?

Only if you're embedding Metabase in a commercial product. For internal use, AGPL is fine. For embedding, you need Metabase's commercial license.

Can I migrate between these tools?

SQL queries transfer between all three. Dashboards and visualizations don't — they're platform-specific. Start with the right tool rather than planning to migrate.

Which handles the most data?

Superset, by design. It's built for large-scale data exploration and connects to high-performance analytical databases (ClickHouse, Druid, BigQuery).

The Verdict

• Evidence for data teams who want version-controlled, reproducible reports. Code-first, Git-native, beautiful output.
• Metabase for teams where business users need self-serve analytics. Easiest to use, best for embedding.
• Superset for enterprise data teams needing powerful visualization and SQL exploration. Most features, most database support.

For most startups: Metabase gets everyone in the company exploring data. For data-mature teams: Evidence for polished reports + Superset for ad-hoc exploration.